{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e60f16d5-0aee-4935-9fd0-0bcbc3905411",
   "metadata": {},
   "source": [
    "# Pandas vs. PyArrow file reading speed comparison\n",
    "## April, 2021\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "812531a4-bb0e-49a2-9604-ec566c8359d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyarrow.parquet as pq\n",
    "import os\n",
    "import sys\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']=125"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c878d7cc-0f58-4674-8055-7567beed9cc2",
   "metadata": {},
   "source": [
    "## Create CSV files of various sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0853d6e6-e6e4-4d6b-8dc0-b3391dd0fea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of file with 5325 rows: 9.999 MB\n",
      "Size of file with 10650 rows: 19.999 MB\n",
      "Size of file with 15975 rows: 30.003 MB\n",
      "Size of file with 21300 rows: 40.01 MB\n",
      "Size of file with 26625 rows: 50.014 MB\n",
      "Size of file with 31950 rows: 60.02 MB\n",
      "Size of file with 37275 rows: 70.024 MB\n",
      "Size of file with 42600 rows: 80.03 MB\n",
      "Size of file with 47925 rows: 90.033 MB\n",
      "Size of file with 53250 rows: 100.039 MB\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,11):\n",
    "    a = np.random.normal(size=(int(5325*i), int(1e2)))\n",
    "    df = pd.DataFrame(a, columns=[\"C\" + str(i) for i in range(100)])\n",
    "    fname = \"test\"+str(i)+\".csv\"\n",
    "    df.to_csv(fname)\n",
    "    print(f\"Size of file with {5325*i} rows: {round(os.path.getsize(fname)/(1024*1024),3)} MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f889311a-b24f-4188-83b7-d4d1aef507d8",
   "metadata": {},
   "source": [
    "## Create Parquet (compressed) files from the same CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "940d6fd5-1c47-4a2d-8b5d-300976a5f354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created test1_parquet.zip\n",
      "Created test2_parquet.zip\n",
      "Created test3_parquet.zip\n",
      "Created test4_parquet.zip\n",
      "Created test5_parquet.zip\n",
      "Created test6_parquet.zip\n",
      "Created test7_parquet.zip\n",
      "Created test8_parquet.zip\n",
      "Created test9_parquet.zip\n",
      "Created test10_parquet.zip\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,11):\n",
    "    fname = \"test\"+str(i)+\".csv\"\n",
    "    parquet_name = \"test\"+str(i)+\"_parquet.zip\"\n",
    "    df = pd.read_csv(fname)\n",
    "    df.to_parquet(parquet_name,compression=\"gzip\")\n",
    "    print(f\"Created {parquet_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3084faf-b242-4fde-849a-ac574fe594e0",
   "metadata": {},
   "source": [
    "The directory should look like this now...\n",
    "\n",
    "![dir](https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Pandas%20and%20Numpy/Read_data_various_sources/dir.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b7f5df-7d78-450d-bf21-309ceaeabde7",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Reading speed of CSV (in Pandas) and Parquet files (with `PyArrow`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f90897c2-c99f-4974-b52f-fa8f34b6f5d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done for file # 1\n",
      "Done for file # 2\n",
      "Done for file # 3\n",
      "Done for file # 4\n",
      "Done for file # 5\n",
      "Done for file # 6\n",
      "Done for file # 7\n",
      "Done for file # 8\n",
      "Done for file # 9\n",
      "Done for file # 10\n"
     ]
    }
   ],
   "source": [
    "t_read_pd,t_read_arrow = [],[]\n",
    "\n",
    "for i in range(1,11):\n",
    "    fname = \"test\"+str(i)+\".csv\"\n",
    "    parquet_name = \"test\"+str(i)+\"_parquet.zip\"\n",
    "    t1 = time.time()\n",
    "    df1 = pd.read_csv(fname)\n",
    "    t2 = time.time()\n",
    "    delta_t = round((t2 - t1), 3)\n",
    "    t_read_pd.append(delta_t)\n",
    "    t1 = time.time()\n",
    "    df2 = pq.read_table(parquet_name)\n",
    "    t2 = time.time()\n",
    "    delta_t = round((t2 - t1), 3)\n",
    "    t_read_arrow.append(delta_t)\n",
    "    print(f\"Done for file # {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9e0f4eff-c3f9-4088-8785-4db160116975",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_read_pd = np.array(t_read_pd)\n",
    "t_read_arrow = np.array(t_read_arrow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "86dfb137-3143-4ac4-970f-2d50cafc0bfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1375x625 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 5))\n",
    "plt.plot(\n",
    "    [10*i for i in range(1, 11)], t_read_pd/t_read_arrow, \"bo--\", linewidth=2, markersize=8\n",
    ")\n",
    "plt.grid(True)\n",
    "plt.title(\n",
    "    \"Ratio of Pandas to Arrow time to read files with increasing size\",\n",
    "    fontsize=16,\n",
    ")\n",
    "plt.xticks([10*i for i in range(1, 11)],fontsize=14)\n",
    "plt.xlabel(\"Size (MB)\", fontsize=14)\n",
    "plt.ylabel(\"Ratio of Pandas/Arrow read time\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0352195e-0d21-4cf3-b953-4c95a0f5dd88",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## What's the order of read time? Seconds, milliseconds?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "hybrid-immigration",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to read 2 columns of a 100 MB (53250 rows) CSV file with Pandas: 1.114 seconds\n"
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "df1 = pd.read_csv(\"test10.csv\", usecols=[\"C1\", \"C99\"])\n",
    "t2 = time.time()\n",
    "delta_t = round((t2 - t1), 3)\n",
    "print(\n",
    "    \"Time taken to read 2 columns of a 100 MB (53250 rows) CSV file with Pandas:\",\n",
    "    delta_t,\n",
    "    \"seconds\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe506be-0aa7-4ae8-9363-a56ff5ae0cb6",
   "metadata": {},
   "source": [
    "#### The reading speed of the 100 MB CSV file with `pd.read_csv()` is about 1.114 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "suffering-stake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C1</th>\n",
       "      <th>C99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.692951</td>\n",
       "      <td>-0.799652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.065218</td>\n",
       "      <td>-0.200694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.023773</td>\n",
       "      <td>-1.928832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.298594</td>\n",
       "      <td>-1.340441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.021031</td>\n",
       "      <td>0.295909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C1       C99\n",
       "0 -0.692951 -0.799652\n",
       "1 -1.065218 -0.200694\n",
       "2  0.023773 -1.928832\n",
       "3 -0.298594 -1.340441\n",
       "4  1.021031  0.295909"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "assumed-harvard",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to read 2 columns of the identical 53250 rows zipped parquet file with PyArrow: 0.026 seconds\n"
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "df2 = pq.read_table(\"test10_parquet.zip\", columns=[\"C1\", \"C99\"])\n",
    "t2 = time.time()\n",
    "delta_t = round((t2 - t1), 3)\n",
    "print(\n",
    "    \"Time taken to read 2 columns of the identical 53250 rows zipped parquet file with PyArrow:\",\n",
    "    delta_t,\n",
    "    \"seconds\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8c9230-179e-45fe-be85-120d8286f5d8",
   "metadata": {},
   "source": [
    "#### The reading speed of the same file (in the parquet gzipped version) with `pq.read_table()` is about 0.026 seconds!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "joint-safety",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C1</th>\n",
       "      <th>C99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.692951</td>\n",
       "      <td>-0.799652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.065218</td>\n",
       "      <td>-0.200694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.023773</td>\n",
       "      <td>-1.928832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.298594</td>\n",
       "      <td>-1.340441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.021031</td>\n",
       "      <td>0.295909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C1       C99\n",
       "0 -0.692951 -0.799652\n",
       "1 -1.065218 -0.200694\n",
       "2  0.023773 -1.928832\n",
       "3 -0.298594 -1.340441\n",
       "4  1.021031  0.295909"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert from PyArrow table to dataframe\n",
    "df3 = df2.to_pandas()\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8339aab-9d39-4e0d-bcdf-21b61368e856",
   "metadata": {},
   "source": [
    "#### The dataframes `df1` and `df3` are the same, as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8dfadbf-918e-4257-8271-076717c3fab3",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Reading a small number of columns is much faster with Arrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "deluxe-absence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done for 2 columns\n",
      "Done for 4 columns\n",
      "Done for 6 columns\n",
      "Done for 8 columns\n",
      "Done for 10 columns\n",
      "Done for 12 columns\n",
      "Done for 14 columns\n",
      "Done for 16 columns\n",
      "Done for 18 columns\n",
      "Done for 20 columns\n",
      "Done for 22 columns\n",
      "Done for 24 columns\n",
      "Done for 26 columns\n",
      "Done for 28 columns\n",
      "Done for 30 columns\n",
      "Done for 32 columns\n",
      "Done for 34 columns\n",
      "Done for 36 columns\n",
      "Done for 38 columns\n",
      "Done for 40 columns\n",
      "Done for 42 columns\n",
      "Done for 44 columns\n",
      "Done for 46 columns\n",
      "Done for 48 columns\n",
      "Done for 50 columns\n",
      "Done for 52 columns\n",
      "Done for 54 columns\n",
      "Done for 56 columns\n",
      "Done for 58 columns\n",
      "Done for 60 columns\n",
      "Done for 62 columns\n",
      "Done for 64 columns\n",
      "Done for 66 columns\n",
      "Done for 68 columns\n",
      "Done for 70 columns\n",
      "Done for 72 columns\n",
      "Done for 74 columns\n",
      "Done for 76 columns\n",
      "Done for 78 columns\n",
      "Done for 80 columns\n",
      "Done for 82 columns\n",
      "Done for 84 columns\n",
      "Done for 86 columns\n",
      "Done for 88 columns\n",
      "Done for 90 columns\n",
      "Done for 92 columns\n",
      "Done for 94 columns\n",
      "Done for 96 columns\n",
      "Done for 98 columns\n"
     ]
    }
   ],
   "source": [
    "all_cols = [\"C\" + str(i) for i in range(100)]\n",
    "t_pandas, t_arrow = [], []\n",
    "for i in range(2, 100, 2):\n",
    "    cols = list(np.random.choice(all_cols, size=i))\n",
    "    t1 = time.time()\n",
    "    df1 = pd.read_csv(\"test10.csv\", usecols=cols)\n",
    "    t2 = time.time()\n",
    "    delta_t_pandas = round((t2 - t1), 3)\n",
    "    t_pandas.append(delta_t_pandas)\n",
    "    t1 = time.time()\n",
    "    df2 = pq.read_table(\"test10_parquet.zip\", columns=cols)\n",
    "    t2 = time.time()\n",
    "    delta_t_arrow = round((t2 - t1), 3)\n",
    "    t_arrow.append(delta_t_arrow)\n",
    "    print(f\"Done for {i} columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "touched-stockholm",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_pandas = np.array(t_pandas)\n",
    "t_arrow = np.array(t_arrow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "behavioral-chester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 5))\n",
    "plt.plot(\n",
    "    [i for i in range(2, 100, 2)], t_pandas / t_arrow, \"bo--\", linewidth=2, markersize=8\n",
    ")\n",
    "plt.grid(True)\n",
    "plt.title(\n",
    "    \"Ratio of Pandas to Arrow time to read a 100 MB CSV file with increasing # of columns\",\n",
    "    fontsize=15,\n",
    ")\n",
    "plt.xticks([i for i in range(0, 100, 10)],fontsize=14)\n",
    "plt.xlabel(\"No. of columns\", fontsize=14)\n",
    "plt.ylabel(\"Ratio of Pandas/Arrow read time\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b4ab83-6b96-4d36-aa4d-476d43130883",
   "metadata": {},
   "source": [
    "## PyArrow (Parquet) reading time varies with sparsity in the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4370323a-7d89-4fe5-8f49-179f1e2aa686",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN-filled file written with 2.28%\n",
      "NaN-filled file written with 5.49%\n",
      "NaN-filled file written with 11.49%\n",
      "NaN-filled file written with 21.21%\n",
      "NaN-filled file written with 34.45%\n",
      "NaN-filled file written with 50.04%\n",
      "NaN-filled file written with 65.54%\n",
      "NaN-filled file written with 78.81%\n",
      "NaN-filled file written with 88.5%\n",
      "NaN-filled file written with 94.53%\n"
     ]
    }
   ],
   "source": [
    "pct_nan = []\n",
    "for i in range(11,21):\n",
    "    a = np.random.normal(size=(int(5325*5), int(1e2)))\n",
    "    cutoff = -2+0.4*(i-11)\n",
    "    a = np.where(a < cutoff, np.nan, a)\n",
    "    p_nan = round(100*np.count_nonzero(np.isnan(a))/a.size,2)\n",
    "    pct_nan.append(p_nan)\n",
    "    df = pd.DataFrame(a, columns=[\"C\" + str(i) for i in range(100)])\n",
    "    fname = \"test\"+str(i)+\".csv\"\n",
    "    df.to_csv(fname)\n",
    "    print(f\"NaN-filled file written with {p_nan}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "132f13c7-583c-46cd-b1db-3a3fc38006b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created test11_parquet.zip\n",
      "Created test12_parquet.zip\n",
      "Created test13_parquet.zip\n",
      "Created test14_parquet.zip\n",
      "Created test15_parquet.zip\n",
      "Created test16_parquet.zip\n",
      "Created test17_parquet.zip\n",
      "Created test18_parquet.zip\n",
      "Created test19_parquet.zip\n",
      "Created test20_parquet.zip\n"
     ]
    }
   ],
   "source": [
    "for i in range(11,21):\n",
    "    fname = \"test\"+str(i)+\".csv\"\n",
    "    parquet_name = \"test\"+str(i)+\"_parquet.zip\"\n",
    "    df = pd.read_csv(fname)\n",
    "    df.to_parquet(parquet_name,compression=\"gzip\")\n",
    "    print(f\"Created {parquet_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0f53bc3-5a73-496a-b574-b5535227c72c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done for file # 11\n",
      "Done for file # 12\n",
      "Done for file # 13\n",
      "Done for file # 14\n",
      "Done for file # 15\n",
      "Done for file # 16\n",
      "Done for file # 17\n",
      "Done for file # 18\n",
      "Done for file # 19\n",
      "Done for file # 20\n"
     ]
    }
   ],
   "source": [
    "t_read_nan = []\n",
    "m_read_nan = []\n",
    "\n",
    "for i in range(11,21):\n",
    "    parquet_name = \"test\"+str(i)+\"_parquet.zip\"\n",
    "    t1 = time.time()\n",
    "    df2 = pq.read_table(parquet_name)\n",
    "    t2 = time.time()\n",
    "    delta_t = round(1000*(t2 - t1), 3)\n",
    "    t_read_nan.append(delta_t)\n",
    "    m_read_nan.append()\n",
    "    print(f\"Done for file # {i}\")\n",
    "t_read_nan=np.array(t_read_nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "531f5393-d837-48b0-bccb-dfbf3562301c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1375x625 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 5))\n",
    "plt.plot(pct_nan,t_read_nan, \"bo--\", linewidth=2, markersize=8)\n",
    "plt.grid(True)\n",
    "plt.title(\"PyArrow (Parquet) reading time varies with sparsity in the file\",fontsize=16,)\n",
    "#plt.xticks([10*i for i in range(1, 11)],fontsize=14)\n",
    "plt.xlabel(\"Sparsity (% of NaN values)\", fontsize=14)\n",
    "plt.ylabel(\"Read time (milliseconds)\", fontsize=14)\n",
    "plt.ylim(int(t_read_nan.min()*0.9),int(t_read_nan.max()*1.1))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
